Generally, image data produced by digitizing an image, especially a moving picture, has a great amount of data, so information compression is performed by coding when the data is transmitted or recorded. Such information compression is carried out on the basis of the partial or temporal correlation of the image data.
As an example of compression coding, there is "prediction coding" in which prediction is performed on the basis of an image close in time to an image being the target of coding (target image), a difference between the target image and the prediction image is obtained as differential data, and the differential data is coded. In the prediction, motion compensation is carried out using a motion vector obtained by motion detection of the image. Generally, the higher the correlation is, the higher the coding efficiency of the differential data is.
On the other hand, in order to transmit or store a moving picture with high efficiency, there is proposed a method in which moving picture data is divided into plural layers corresponding to individual objects included in the picture, and each layer is coded. For example, in the case of coding an image composed of a person and a background, initially, an image coding apparatus divides the image into two layers for the person and the background, respectively, encodes the respective layers, and transmits the coded data of the respective layers. On the other hand, in an image decoding apparatus, the transmitted coded data of the respective layers are decoded, and decoded images of the respective layers are synthesized using a prescribed method and displayed.
Further, in the above-mentioned synthesis process, information showing, pixel by pixel, whether the background object is hidden or not by the overlapping of the images, is required. This information is called "a significant signal", and pixels hiding the background are called "significant pixels". A large value of the significant signal means that the ratio of synthesis is large and the signal is important visually. To the contrary, a small value of the significant signal means that it is hardly used for synthesis, i.e., it is almost transparent.
As described above, a significant signal shows the shape of an image synthesized with a background, and only significant pixels influence on the quality of the synthesized image. In other words, insignificant pixels have no relation with the image quality, so that the efficiency in coding can be improved by coding only significant pixels.
Meanwhile, a method for orthogonally transforming only significant pixels, called "Shape Adaptive Orthogonal Transform (SADCT)", is disclosed in IEEE Transactions on Circuits and Systems for Video Technology vol.5, No.1, February 1995. In this method, only significant pixels are subjected to orthogonal transform.
On the other hand, as a method for generating a prediction image, "Overlap Motion Compensation" is disclosed in ITU-T Recommendation H.263, pp. 42-44, October 1995. In this method, an optimum prediction area is calculated by interpolating plural small areas within a prediction image.
However, since compression by coding is based on spatial or temporal correlation in an image, if insignificant pixels are coded as the target of coding together with significant pixels, the coding efficiency is reduced. For example, when all the significant pixels are black, if the insignificant pixels are black as well, the coding efficiency is not reduced. However, if the insignificant pixels are white, since the correlation is reduced, the coding efficiency is reduced. Generally, when insignificant pixels are included in an area surrounded by significant pixels, the inter-pixel correlation is reduced, whereby the coding efficiency is reduced.
Further, also in the above-mentioned SADCT, when, in a target image, insignificant pixels are included in an area surrounded by significant pixels, the correlations in the vertical and horizontal directions are reduced, whereby the coding efficiency is reduced.
As described above, although the pixel values of insignificant pixels do not have much influence on the quality of reproduced image, these pixel values adversely affect the coding efficiency. Therefore, when coding is performed, the pixel values of insignificant pixels must be considered. In the prior art methods, however, this matter has not bee considered at all.